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cs.CV2026
TTF: Temporal Token Fusion for Efficient Video-Language Model
Simin Huo, Ning LI
Video-language models (VLMs) face rapid inference costs as visual token counts scale with video length. For example, 32 frames at resolution already yield >8,000 v…
cs.CV2026
Boosting Self-Supervised Tracking with Contextual Prompts and Noise Learning
Yaozong Zheng, Qihua Liang, Bineng Zhong +4
Learning robust contextual knowledge from unlabeled videos is essential for advancing self-supervised tracking. However, conventional self-supervised trackers lack effective contex…
cs.CV2026
MaMe & MaRe: Matrix-Based Token Merging and Restoration for Efficient Visual Perception and Synthesis
Simin Huo, Ning Li
Token compression is crucial for mitigating the quadratic complexity of self-attention mechanisms in Vision Transformers (ViTs), which often involve numerous input tokens. Existing…